ASU Electronic Theses and Dissertations
- 3 English
- 3 Public
- Deep Learning
- 3 Computer science
- 2 Artificial intelligence
- 1 Convolutional Neural Networks
- 1 Dataset Generality
- 1 Feature Engineering
- 1 Image Aesthetics
- 1 Image Representations
- 1 Learning Representations
- 1 Machine Learning
- 1 Mentee Networks
- 1 Multiple Instance Learning
- 1 Ranking
- 1 Siamese Networks
- 1 Visual Computing
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, a novel problem of ranking images with respect to their aesthetic quality is formulated ...
- Gattupalli, Jaya Vijetha R., Li, Baoxin, Davulcu, Hasan, et al.
- Created Date
Computer Vision as a eld has gone through signicant changes in the last decade. The eld has seen tremendous success in designing learning systems with hand-crafted features and in using representation learning to extract better features. In this dissertation some novel approaches to representation learning and task learning are studied. Multiple-instance learning which is generalization of supervised learning, is one example of task learning that is discussed. In particular, a novel non-parametric k- NN-based multiple-instance learning is proposed, which is shown to outperform other existing approaches. This solution is applied to a diabetic retinopathy pathology detection problem eectively. In cases ...
- Venkatesan, Ragav, Li, Baoxin, Turaga, Pavan, et al.
- Created Date
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can ...
- Chandakkar, Parag Shridhar, Li, Baoxin, Yang, Yezhou, et al.
- Created Date
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries.
For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at firstname.lastname@example.org.